Patient monitoring, clinical waveforms, medical wearables and Internet of Medical Things (IoMT) network traces for health informatics and machine learning research.
Internet of Medical Things IDS dataset with BLE, IP packet, and IP flow captures. CSV and pickle formats, 2.7 GB, for ML-based healthcare IoT intrusion detection.
Open-access wearable gait dataset with 400 CSV files from 20 healthy participants across 5 smartphone orientations and 2 gait trial types. 100 Hz IMU sampling. Used for smartphone-based gait metric validation and wearable sensor accuracy studies.
Large-scale quality-assessed ICU PPG benchmark derived from MIMIC-III, with ECG, ABP, and respiration signals in 30-second WFDB segments. Multi-task format supporting cardiovascular and respiratory signal analysis for wearable algorithm development.
Real-world wearable dataset from 15 nurses over one week in a hospital. Contains 11.5 million entries of EDA, heart rate, skin temperature, and orientation data collected via Empatica E4. CSV format. Used for occupational stress detection research.
Multimodal physiological dataset from 15 subjects wearing chest and wrist sensors. Includes ECG, EDA, EMG, respiration, temperature, and accelerometry. CSV/pickle format. Used for stress detection and affective computing research.
Free CC0 synthetic dataset: 1,000 rows of heart rate, blood pressure, SpO2, body temperature and glucose vitals. 3 Age Groups, 7 Patients.
Synthetic healthcare dataset for data science, machine learning, and data analysis projects in medical research.
MIMIC-III is a globally recognized database featuring de-identified health data from 40,000+ ICU patients, integrating vital signs, lab results, and IoT device outputs for research.
MC-MED provides high-resolution multimodal clinical and physiological data from 118,385 adult emergency department visits, supporting real-time monitoring and AI-based medical research.
Large public dataset from Microsoft Research Aurora Project with ECG and PPG signals from wrist-worn wearables, balanced for gender, age, and hypertension status, enabling causal inference research for non-invasive blood pressure prediction with 205 extracted features.
67,830 record sets of multi-channel physiologic waveforms (ECG, ABP, respiration, PPG) and vital sign time series from approximately 30,000 ICU patients, supporting clinical research and ML model development for critical care.
48 half-hour excerpts of two-channel ambulatory ECG recordings from 47 subjects (1975–1979), digitized at 360 Hz with expert annotations for approximately 110,000 heartbeats, widely used for arrhythmia detection research.[page:2][web:219]
Multi-source healthcare dataset integrating Electronic Health Records, medical imaging (CT and MRI scans), and wearable IoT sensor data for personalized treatment optimization. Includes 5,008 brain imaging files and real-time physiological monitoring data.
IoT-based environmental perception data studying impact on university students' mental health. Integrates temperature, humidity, noise, and air quality sensors.
High-fidelity biometric signals from wearable IoT nodes, including multi-lead ECG and 3-axis motion data for remote healthcare applications.
Real-time physiological and network-level data from a secure IoT healthcare monitoring system tracking 2000 patients, including biometric readings (heart rate, temperature, blood pressure) and network metadata for anomaly detection and cybersecurity analysis.
Real-time IoT sensor data from wearable health monitoring devices tracking patient vital signs including body temperature, blood pressure (systolic/diastolic), heart rate, and device battery levels for remote healthcare monitoring and predictive analytics.
Simulated sensor data from IoT-based wearable healthcare devices monitoring vital signs including temperature, blood pressure, heart rate, and device battery levels for real-time remote patient monitoring and health analytics applications.
Real-world dataset from International University of Rabat for threat detection in MQTT-IoT networks, containing actual cyberattacks executed on MySignals health sensors.
Benchmark dataset for Internet of Medical Things (IoMT) security research. Contains 18 different cyberattack types targeting 40 IoMT devices across diverse medical protocols.
Simulated sensor data from wearable IoT devices for remote patient health monitoring. Includes vital signs (temperature, blood pressure, heart rate) with timestamps and device battery levels.
Realistic benchmark dataset for Internet of Medical Things (IoMT) security research. Captures biomedical sensor data and network traffic from medical devices including ECG monitors, pulse oximeters, and other healthcare IoT equipment.
Clinical recordings of individual and mixed heart/lung sounds captured via digital stethoscope IoT sensors.
Time-series data from wearable medical IoT devices tracking cardiac activity during various physical states.